本研究旨在探討K線技術分析於ETF市場中的預測效能與實務應用價值。儘管K線分析作為技術分析的重要工具，其於股票市場中已有廣泛應用與研究，然而在ETF市場快速發展且結構獨特的情境下，其有效性尚缺乏系統性的實證檢驗。本文以美國ETF市場332檔ETF於2014年11月至2019年11月之日交易資料為研究樣本，運用Fock et al. (2005) 與Lu et al. (2015)所提出之18種常見K線型態，以確保研究過程之客觀性與可重複性，並透過自動化掃描與交易模擬計算短期（3日）平均報酬率進行驗證。
為符合實務上的合理性，研究結果接扣除0.25%之交易成本，並以偏態調整t檢定評估報酬顯著性，同時以ETF類別（市值型、股票型、策略型、商品與不動產型、債券型），以確認結果在不同市場環境下之穩健性。此外，本文亦考量資料窺探問題對技術分析研究結果的潛在影響，參考White (2000) 與 Sullivan et al. (1999) 方法進行多重檢定控制，以降低虛假發現率。
實證結果顯示，部分K線型態於ETF市場中具備顯著且穩健的預測能力，尤其於特定ETF類別及市場條件下，能有效捕捉短期市場動能，提升績效。然而，不同K線型態於各類ETF與市場週期中的績效差異顯著，顯示技術分析策略在應用時需考量市場特性與策略調適的重要性。本研究提供實證支持K線技術分析於ETF市場之應用潛力，並為技術分析於被動投資工具市場的有效性檢驗提供理論基礎與實務參考。

This study investigates the predictive power and practical implications of candlesticktechnical analysis in the ETF market. While candlestick patterns have been widely studied in stock markets, their validity in the rapidly growing and structurally unique ETF market remains underexplored. Using daily trading data of 332 U.S. ETFs from November 2014 to November 2019, this study applies 18 commonly used candlestick patterns defined by Fock et al. (2005) and Lu et al. (2015). An automated detection system and trading simulations are employed to evaluate short-term (three-day) average returns.
To ensure practical relevance, all returns are adjusted for a 0.25% transaction cost, with statistical significance assessed via a skewness-adjusted t-test. Sub-sample analyses acrossETF categories—including equity, strategy-based, commodity & real estate, and bond ETFs—are conducted to test robustness. Furthermore, the study controls for data-snooping biases by adopting multiple testing adjustments proposed by White (2000) and Sullivan et al. (1999).
The empirical results demonstrate that certain candlestick patterns possess significant and robust predictive ability in ETF markets, particularly under specific ETF categories and market conditions. However, substantial performance heterogeneity exists across patterns, implying the importance of market context and strategy adaptation. These findings provide empirical support for the application of candlestick analysis in ETF markets and contribute to the literature on the effectiveness of technical analysis in passive investment instruments.

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